Kumo Co-Founder Hema Raghavan Named to Inc.’s 2026 Female Founders 500
Entity Resolution & Identity
Your data is fragmented across systems. Kumo uses graph-based resolution to match identities, deduplicate records, and link entities — going far beyond fuzzy string matching to understand true relational similarity.
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6
Prediction types
Identity to cross-device
0
Rules to maintain
Learned automatically
<1 hr
To production
Per use case
0
Third-party cookies
Behavioral matching
Loved by data scientists, ML engineers & CXOs at
6 entity resolution use cases
From identity matching to cross-device linking — graph-based resolution that goes beyond string similarity.
Match customer identities across databases, channels, and touchpoints using relational structure - not just fuzzy string matching.
Find and merge duplicate records in CRM, ERP, and master data systems using graph-based similarity.
Link records across systems - transactions to customers, events to entities - using learned relational patterns.
Identify household and family relationships across individual records for unified customer views.
Merge duplicate accounts across CRM, billing, and support systems using relational signals beyond name and email matching.
Link user activity across devices and browsers using behavioral and relational patterns - without relying on third-party cookies.
See how graph-learning AI compares to rules-based matching and manual ETL.
Matching approach
Traditional
Fuzzy string matching + rules
With Kumo
Graph-based relational similarity
Cross-system linking
Traditional
Manual ETL mapping
With Kumo
Learned from relational patterns
Household mapping
Traditional
Address-based heuristics
With Kumo
Behavioral + transactional graph
Cross-device
Traditional
Third-party cookies
With Kumo
Behavioral relational patterns
Maintenance
Traditional
Rules need constant updating
With Kumo
Model learns automatically
Setup time
Traditional
Months of rules engineering
With Kumo
Under 1 hour
Simply connect your data, start asking predictions, and get results.Want more control? Fine-tune the model for your specific use case.
Integrates directly with your warehouse, no additional pipeline setup.
Ask questions in plain English and let Kumo do the modeling for you.
Get clear predictions and push them instantly into your workflows.
PREDICT COUNT(transactions.*, 0, 90, days) = 0
FOR EACH customers.customer_id
WHERE COUNT(transactions.*, -60, 0, days) > 03 lines. No feature engineering. No pipeline code.
For developers
Kumo's Predictive Query Language (PQL) replaces months of feature engineering, model training, and pipeline work with a few lines of SQL-like syntax. Describe what you want to predict — Kumo handles the rest.
Get accurate predictions on relational data instantly—no training or ML setup required.
Read the KumoRFM announcement
Vanja Josifovski
CEO and Co-Founder
Former CTO at Airbnb and Pinterest
Jure Leskovec
Co-Founder & Chief Scientist
Stanford Professor · Co-creator of RDL and GNN
Hema Raghavan
Co-Founder & Head of Engineering
Former Sr. Director of Engineering at LinkedIn
Loved by data scientists, ML engineers & CXOs at
Peer-reviewed
Kumo is built on 40+ peer-reviewed papers at NeurIPS, ICML, and KDD. The methodology is public and reproducible.
K. Huang, M. Fey, J. Leskovec et al.
A foundation model for relational data — pre-trained across schemas, it delivers accurate predictions out of the box and improves with fine-tuning.
Read paperM. Fey, W. Hu, K. Huang, J. Leskovec et al.
Learning predictive models directly on relational databases, eliminating the feature engineering pipeline.
Read paperJ. Robinson, R. Miao, K. Huang et al.
An open benchmark for evaluating relational prediction methods across 11 databases and 30 tasks.
Read paperSee what Kumo can resolve from your existing relational database.
